The physics of news, rumors, and opinions(arxiv.org)
arxiv.org
The physics of news, rumors, and opinions
https://arxiv.org/abs/2510.15053
36 comments
"All models are wrong; but some are useful." - George Box
Even particles aren't actually particles, nor spin states actually spin states. The map is not the territory. Physics models are only useful (and "correct") to the extent that they make successful predictions. If the adaptation of these principles to social communication yields useful predictions, then however inaccurate they may be in reproducing the exact nature of what they model, they are nonetheless useful and therefore worthwhile. FTA: "In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact."
Even particles aren't actually particles, nor spin states actually spin states. The map is not the territory. Physics models are only useful (and "correct") to the extent that they make successful predictions. If the adaptation of these principles to social communication yields useful predictions, then however inaccurate they may be in reproducing the exact nature of what they model, they are nonetheless useful and therefore worthwhile. FTA: "In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact."
Natural scientific models use the vocabulary of pre-existing intuitions we have from interacting with the world: particles, rotation, etc. We try to simplify predictions by mapping experimental outcomes using things we already know and can directly perceive.
Using these abstractions as foundation for models of social dynamics and modern media feels a little wrong: the mapping is imperfect and incomplete, and as these physical models inevitably become outdated it will be more and more difficult to make sense of social dynamics models that use these physical models as a given.
Using these abstractions as foundation for models of social dynamics and modern media feels a little wrong: the mapping is imperfect and incomplete, and as these physical models inevitably become outdated it will be more and more difficult to make sense of social dynamics models that use these physical models as a given.
Agreed. But when you have a hammer, everything looks like a nail.
The word “some” in the quote from Box is doing a lot of heavy lifting.
If a model is useful, I’d like to see it being used (outside academia, where there’s minimal penalty for complexity and a high emphasis on novelty).
If models like these are widely adopted at social media companies or news agencies, it’s fair to say OP’s take isn’t valid. Otherwise they may have a point.
If a model is useful, I’d like to see it being used (outside academia, where there’s minimal penalty for complexity and a high emphasis on novelty).
If models like these are widely adopted at social media companies or news agencies, it’s fair to say OP’s take isn’t valid. Otherwise they may have a point.
Agreed. But these kinds of models almost always start in academia, that's one of the big reasons we have academia, to explore ideas that may (or may not) be useful. My point was that you can't prejudge the usefulness of a model simply because it doesn't fully replicate all the complexity of the phenomenon being modeled.
These ideas are used, and they influence what policy is crafted.
You can’t predict what an individual will do, but work like this kills many inaccurate ideological positions that we inherited.
There’s a paper from 2016 that shows how posts saturate/cascade through conspiracy communities and that it has distinct cascade dynamics. This wasn’t a model, it was a description of observed behavior.
Or take some relatively recent work from Harvard, which suggests that while our capacity to create misinformation has increased in both quantity and quality, its consumption rate seems to be stable.
You can’t predict what an individual will do, but work like this kills many inaccurate ideological positions that we inherited.
There’s a paper from 2016 that shows how posts saturate/cascade through conspiracy communities and that it has distinct cascade dynamics. This wasn’t a model, it was a description of observed behavior.
Or take some relatively recent work from Harvard, which suggests that while our capacity to create misinformation has increased in both quantity and quality, its consumption rate seems to be stable.
> kills many inaccurate ideological positions that we inherited.
It doesn't, which is part of the point the OP is making. And now my point, it's ok that these pseudo-scientific "revelations" don't kill those "inaccurate ideological positions", because that's the whole point of human free will, there's no "accurate ideological position" when it comes to the day-to-day life, or to societal life in general.
It doesn't, which is part of the point the OP is making. And now my point, it's ok that these pseudo-scientific "revelations" don't kill those "inaccurate ideological positions", because that's the whole point of human free will, there's no "accurate ideological position" when it comes to the day-to-day life, or to societal life in general.
I have used them for moderation and policy, back when I used to moderate.
I retired ideological beliefs in favor of reality when I went and found the data and research.
Stating “it doesn’t”, does not convert an opinion into fact.
That said, I suspect you haven’t read the paper and are arguing from the headline.
My intuition is that you will find the research complimentary to your ideas, and not in opposition.
I retired ideological beliefs in favor of reality when I went and found the data and research.
Stating “it doesn’t”, does not convert an opinion into fact.
That said, I suspect you haven’t read the paper and are arguing from the headline.
My intuition is that you will find the research complimentary to your ideas, and not in opposition.
> That said, I suspect you haven’t read the paper and are arguing from the headline.
You're correct on that, as I find that applying the word "science", or "research", or "paper", to the day-to-day life, like to news article in this case, is not science per se, and unfortunately I don't have time to lose on drivel that paints itself as science (but which it is not science, as I mentioned above).
You're correct on that, as I find that applying the word "science", or "research", or "paper", to the day-to-day life, like to news article in this case, is not science per se, and unfortunately I don't have time to lose on drivel that paints itself as science (but which it is not science, as I mentioned above).
People are not that complicated, and there's abundant evidence that physical models of social phenomena have decent predictive power, notwithstanding all the intervening complexity. The argument of statistical physics is not that people are particles, but that the equations we've found good at describing various natural phenomena work in social contexts because those processes tend toward efficiently conserving energy and the like. Same reason many human and animal activities tend toward power-law distributions.
You should deploy a quant trading model based on your predictive behavior theory and compound your way to be the richest person on earth. It's not that complicated apparently!
I think GP meant to say that people are not that sophisticated. Newtonian physics is not very sophisticated but fortunes have made and broke on deploying it (Roulette, dice, coinflipping etc)
statistical physics, otoh, is slightly more sophisticated than the median human so some experts have indeed made money building related models
statistical physics, otoh, is slightly more sophisticated than the median human so some experts have indeed made money building related models
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So you are saying that if there exist quant traders making lots of money off their models, then that would be evidence that human behavior can be accurately mathematically modeled?
It's a lot more complicated than that. Eugene Fama might be a good starting point for you.
(Spoiler: most PhD level machine learning quant trading funds do not make money. Marcos Lopez de Prado can explain why that is the case: they accidentally invest in false discoveries. It's so pervasive that I often argue it's a numerical analytic proof for EMH. Most quant funds you've heard of are engaged in high-frequency market making, they provide liquidity by continuously quoting prices and earning the bid-ask spread with limited directional risk. It's not behavior forecasting. Something like order flow toxicity has predictive ability for like a few milliseconds.)
Something like this does not work: https://www.tradingview.com/v/9Ut0yL2p/
Even if it has a good p-hacked backtest with some parameters, it's not going to work in practice. It's just a novelty by/for academics.
(Spoiler: most PhD level machine learning quant trading funds do not make money. Marcos Lopez de Prado can explain why that is the case: they accidentally invest in false discoveries. It's so pervasive that I often argue it's a numerical analytic proof for EMH. Most quant funds you've heard of are engaged in high-frequency market making, they provide liquidity by continuously quoting prices and earning the bid-ask spread with limited directional risk. It's not behavior forecasting. Something like order flow toxicity has predictive ability for like a few milliseconds.)
Something like this does not work: https://www.tradingview.com/v/9Ut0yL2p/
Even if it has a good p-hacked backtest with some parameters, it's not going to work in practice. It's just a novelty by/for academics.
The question is not "do all quants make money modelling human behavior?" It's "do any quants make money modelling human behavior?"
And are there any quant funds that consistently make money? No. It's like professional gambling. Susquehanna actually bets on sports too. Constant losses (hopefully slightly) offset by gains. There's no such as a trader with high accuracy/precision. If you can even edge out 1% (gain-loss) you'll be a billionaire from $0 very quickly. A positive edge is so exceptionally rare, it's like a startup -> unicorn. It would be the equivalent of owning a casino with trillions of dollars being played nonstop. It almost never happens.
If there was any fixed analytic solution to behavior then a simple neural network would approximate it quite easily. It's almost all noise with only spurious correlations and misleading patterns that don't repeat.
If there was any fixed analytic solution to behavior then a simple neural network would approximate it quite easily. It's almost all noise with only spurious correlations and misleading patterns that don't repeat.
Did you read the paper? Or even the intro? If a model has predictive power, it's capturing something, end of story. What you do with it, popsci spins it, how you interpret it has nothing to do with whether or not it's useful. That's your projection. Everything you are saying it doesn't do as if it's an argument against the paper happens to overlap perfectly with the things it never claimed to do.
Predictive power alone doesn’t equal causal understanding. The paper models news and opinion spread as physical processes that may (over)fit observed data, but it never establishes why these patterns occur. No counterfactuals, no intervention logic, no identification strategy. As causal inference work (like Stefan Wager's) makes clear, explanation demands more than correlation. Treating human communication as node-to-node contagion might predict past outcomes, but it misses the purposive, context-driven nature of choice. So while the model captures statistical regularities, it lacks the causal rigor needed to claim genuine understanding of human behavior.
I'm assuming you've never predicted things in practice for a living? e.g. as a quant trader? Quants have something called a "deflated sharpe ratio" since p-hacking / overfitting historical data is such a common thing and results in losses when projected into the future.
I'm assuming you've never predicted things in practice for a living? e.g. as a quant trader? Quants have something called a "deflated sharpe ratio" since p-hacking / overfitting historical data is such a common thing and results in losses when projected into the future.
Gas isn't a continuous medium, either; it's made up of particles. (Not even true particles, either, and sometimes that matters.) We can still create equations for sound waves, though.
Now, sure, humans are more complicated than gas molecules, and have an element of choice in what they do. But in bulk, they still behave in ways that can be modeled mathematically - perhaps not perfectly, but enough that it can still give some actual insight.
Now, sure, humans are more complicated than gas molecules, and have an element of choice in what they do. But in bulk, they still behave in ways that can be modeled mathematically - perhaps not perfectly, but enough that it can still give some actual insight.
are you claiming that human action is not measurable physical phenomena?
Human action is entirely causally dependent on human psychology vis a vis biology, of which we have now only a rudimentary formal understanding and certainly not a sufficient model of its structure, let alone the relation between its aspects and the resulting actions.
At the same time, statistical methods are interesting and suggestive but should be understood at the relatively coarse level they inhabit.
Both approaches have their uses and it is worth delineating the boundary between their respective appropriate contexts.
At the same time, statistical methods are interesting and suggestive but should be understood at the relatively coarse level they inhabit.
Both approaches have their uses and it is worth delineating the boundary between their respective appropriate contexts.
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Human action isn’t just another physical process because it’s driven by intention, not mechanical causation. A rock falls because gravity compels it; a person acts because they want something to happen. That difference makes human behavior fundamentally qualitative. It’s rooted in meaning, interpretation, and choice. You can measure motion, but you can’t measure purpose. Once you strip away intention to fit behavior into a mathematical model, you’re no longer describing human action. You’re describing an abstraction that behaves like a machine. The numbers might be tidy, but they stop representing what people actually do.
Imagine thinking that a statistical model should address individuals' internal state. That's like considering thermodynamics bunk because it addresses aggregate behavior without accounting for that of individual particles.
Wow man. Thermodynamics operates on mechanical causality while human behavior does not. Because of the internal state of a human.
Do you just believe anything that was p-hacked together? I can tell you have never predicted human behavior in practice for a living. If you're so interested, why not learn the basics of what quant traders do to predict behavior? Then your opinions would be a little more informed and not quite so embarrassing for yourself. Just a suggestion.
Also, if A implies B and B implies C then A implies C. You're welcome!!
Do you just believe anything that was p-hacked together? I can tell you have never predicted human behavior in practice for a living. If you're so interested, why not learn the basics of what quant traders do to predict behavior? Then your opinions would be a little more informed and not quite so embarrassing for yourself. Just a suggestion.
Also, if A implies B and B implies C then A implies C. You're welcome!!
Ooh my kind of paper.
Also an older paper that I was introduced to, that exposed me to the concept of cascades sizes.
https://www.pnas.org/doi/10.1073/pnas.1517441113
And they went into it too:
> xtending the analysis to 50,220 fact-checking posts from dedicated debunking pages, Zollo et al. [288] find that corrective information remains almost entirely confined to the scientific echo chamber: approximately two-thirds of likes on debunk-ing posts come from science-oriented users, and only a small fraction of conspiracy-oriented users engage with such content. Sentiment analysis of comments, based on a supervised clas- sification model, reveals that responses to debunking posts are predominantly negative, regardless of the commenter’s orienta-tion. Strikingly, the rare conspiracy users who do interact with debunking tend to increase their subsequent activity within the conspiracy echo chamber, suggesting that exposure to dissent-ing information can reinforce rather than attenuate prior beliefs. Otherwise stated, results indicate that the spread of misinfor-mation online is less a problem of information scarcity than of entrenched structural and cognitive segregation, where homo-geneity and polarization govern the dynamics of both the diffu-sion of false narratives and the reception of their correction.
Use this the next time someone promises that online conversations naturally tend towards insight.
Also an older paper that I was introduced to, that exposed me to the concept of cascades sizes.
https://www.pnas.org/doi/10.1073/pnas.1517441113
And they went into it too:
> xtending the analysis to 50,220 fact-checking posts from dedicated debunking pages, Zollo et al. [288] find that corrective information remains almost entirely confined to the scientific echo chamber: approximately two-thirds of likes on debunk-ing posts come from science-oriented users, and only a small fraction of conspiracy-oriented users engage with such content. Sentiment analysis of comments, based on a supervised clas- sification model, reveals that responses to debunking posts are predominantly negative, regardless of the commenter’s orienta-tion. Strikingly, the rare conspiracy users who do interact with debunking tend to increase their subsequent activity within the conspiracy echo chamber, suggesting that exposure to dissent-ing information can reinforce rather than attenuate prior beliefs. Otherwise stated, results indicate that the spread of misinfor-mation online is less a problem of information scarcity than of entrenched structural and cognitive segregation, where homo-geneity and polarization govern the dynamics of both the diffu-sion of false narratives and the reception of their correction.
Use this the next time someone promises that online conversations naturally tend towards insight.
Harry Seldon et. Al
So, an Ising model?
(reads the article)
It's an Ising model!
(reads the article)
It's an Ising model!
as always, Douglas Adams was here first, with the idea if FTL travel powered by bad news, but as the ships were so unwelcome wherever they arrived, the idea had to be abandoned
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Physics operates on mechanical causality while human behavior does not.